# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for IDEFICS2.
"""

from typing import TYPE_CHECKING, Dict, List, Optional, Union

from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, is_valid_image, load_image
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import AddedToken, BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
from ...utils import TensorType, logging


if TYPE_CHECKING:
    from ...pipelines.conversational import Conversation
    from ...tokenization_utils_base import PreTokenizedInput


logger = logging.get_logger(__name__)


def is_url(val) -> bool:
    return isinstance(val, str) and val.startswith("http")


def is_image_or_image_url(elem):
    return is_url(elem) or is_valid_image(elem)


class Idefics2Processor(ProcessorMixin):
    r"""
    Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor.

    [`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See
    the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.

    Args:
        image_processor (`Idefics2ImageProcessor`):
            An instance of [`Idefics2ImageProcessor`]. The image processor is a required input.
        tokenizer (`PreTrainedTokenizerBase`, *optional*):
            An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
        image_seq_len (`int`, *optional*, defaults to 64):
            The length of the image sequence i.e. the number of <image> tokens per image in the input.
            This parameter is used to build the string from the input prompt and image tokens and should match the
            config.perceiver_config.resampler_n_latents value for the model used.
    """

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "Idefics2ImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor, tokenizer=None, image_seq_len: int = 64, **kwargs):
        if image_processor is None:
            raise ValueError("You need to specify an `image_processor`.")
        if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`.")

        self.fake_image_token = AddedToken("<fake_token_around_image>", normalized=False, special=True)
        self.image_token = AddedToken("<image>", normalized=False, special=True)
        self.end_of_utterance_token = AddedToken("<end_of_utterance>", normalized=False, special=True)
        self.image_seq_len = image_seq_len

        tokens_to_add = {
            "additional_special_tokens": [self.fake_image_token, self.image_token, self.end_of_utterance_token]
        }
        tokenizer.add_special_tokens(tokens_to_add)

        # Stores a Jinja template that formats chat histories into tokenizable strings
        self.chat_template = kwargs.pop("chat_template", None)

        super().__init__(image_processor, tokenizer)

    def _extract_images_from_prompts(self, prompts):
        prompt_images = []
        for prompt in prompts:
            images = []
            for elem in prompt:
                if is_valid_image(elem):
                    images.append(elem)
                elif is_url(elem):
                    images.append(load_image(elem))
            prompt_images.append(images)
        return prompt_images

    def __call__(
        self,
        text: Union[TextInput, "PreTokenizedInput", List[TextInput], List["PreTokenizedInput"]] = None,
        images: Union[ImageInput, List[ImageInput], List[List[ImageInput]]] = None,
        image_seq_len: Optional[int] = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        is_split_into_words: bool = False,
        add_special_tokens: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
    ) -> BatchEncoding:
        """
        Processes the input prompts and returns a BatchEncoding.

        Example:

        ```python
        >>> import requests
        >>> from transformers import Idefics2Processor
        >>> from transformers.image_utils import load_image

        >>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2)
        >>> processor.image_processor.do_image_splitting = False  # Force as False to simplify the example

        >>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
        >>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"

        >>> image1, image2 = load_image(url1), load_image(url2)
        >>> images = [[image1], [image2]]

        >>> text = [
        ...     "<image>In this image, we see",
        ...     "bla bla bla<image>",
        ... ]
        >>> outputs = processor(text=text, images=images, return_tensors="pt", padding=True)
        >>> input_ids = outputs.input_ids
        >>> input_tokens = processor.tokenizer.batch_decode(input_ids)
        >>> print(input_tokens)
        ['<s><fake_token_around_image><image><image><fake_token_around_image> In this image, we see', '<s> bla bla bla<fake_token_around_image><image><image><fake_token_around_image>']
        ```

        Args:
            text (`Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).

                Wherever an image token, `<image>` is encountered it is expanded to
                `<fake_token_around_image>` + `<image>` * `image_seq_len` * <fake_token_around_image>`.
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. If is of type `List[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
            image_seq_len (`int`, *optional*):
                The length of the image sequence. If not provided, the default value is used.
            padding (`Union[bool, str, PaddingStrategy]`, *optional*, defaults to `False`):
                Padding strategy applied to the input ids. See [`PreTrainedTokenizerFast.pad`] for more information.
            truncation (`Union[bool, str, TruncationStrategy]`, *optional*):
                Truncation strategy applied to the input ids. See [`PreTrainedTokenizerFast.truncate`] for more information.
            max_length (`int`, *optional*):
                Maximum length of the returned list and optionally padding/truncation length. See
                [`PreTrainedTokenizerFast.__call__`] for more information.
            is_split_into_words (`bool`, *optional*, defaults to `False`):
                Whether the input text is split into words or not. If set to `True`, the tokenizer will skip the
                tokenization process and assume the input is already tokenized.
            add_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether to add special tokens or not. See [`PreTrainedTokenizerFast.__call__`] for more information.
            return_tensors (`Union[str, TensorType]`, *optional*):
                If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
                information.
        """
        image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len

        n_images_in_text = []
        inputs = BatchFeature()

        if text is not None:
            if isinstance(text, str):
                text = [text]
            elif not isinstance(text, list) and not isinstance(text[0], str):
                raise ValueError("Invalid input text. Please provide a string, or a list of strings")

            # Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
            fake_image_token = self.fake_image_token.content
            image_token = self.image_token.content
            image_str = f"{fake_image_token}{image_token * image_seq_len}{fake_image_token}"

            if self.image_processor.do_image_splitting:
                # A single image token is split into 4 patches + 1 original image
                image_str = image_str * 5

            prompt_strings = []
            for sample in text:
                n_images_in_text.append(sample.count(image_token))
                sample = sample.replace(image_token, image_str)
                # Remove any double fake tokens if images are adjacent
                sample = sample.replace(f"{fake_image_token}{fake_image_token}", f"{fake_image_token}")
                prompt_strings.append(sample)

            text_inputs = self.tokenizer(
                text=prompt_strings,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                is_split_into_words=is_split_into_words,
                return_tensors=return_tensors,
            )
            inputs.update(text_inputs)

        if images is not None:
            if is_image_or_image_url(images):
                images = [[images]]
            elif isinstance(images, list) and is_image_or_image_url(images[0]):
                images = [images]
            elif (
                not isinstance(images, list)
                and not isinstance(images[0], list)
                and not is_image_or_image_url(images[0][0])
            ):
                raise ValueError(
                    "Invalid input images. Please provide a single image or a list of images or a list of list of images."
                )

            n_images_in_images = [len(sample) for sample in images]
            if text is not None and not n_images_in_images == n_images_in_text:
                raise ValueError(
                    f"The number of images in the text {n_images_in_text} and images  {n_images_in_images} should be the same."
                )

            # Load images if they are URLs
            images = [[load_image(im) for im in sample] for sample in images]
            image_inputs = self.image_processor(images, return_tensors=return_tensors)
            inputs.update(image_inputs)

        return inputs

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

    def apply_chat_template(
        self,
        conversation: Union[List[Dict[str, str]], "Conversation"],
        chat_template: Optional[str] = None,
        tokenize: bool = False,
        **kwargs,
    ) -> str:
        """
        Overrides the tokenizer's `apply_chat_template` method to apply the IDEFICS2 chat template by default
        if no chat template is provided.

        By default, the output isn't tokenized. This is because the IDEFICS2 chat template is designed to insert
        the image token <image> into the sequence according to the message, but does not handle expanding the image
        tokens to the sequence length or adding the surrounding tokens e.g. <fake_image_token>.

        Args:
            conversation (`Union[List[Dict, str, str], "Conversation"]`):
                The conversation to format.
            chat_template (`Optional[str]`, *optional*):
                The Jinja template to use for formatting the conversation. If not provided, the default chat template
                is used.
            tokenize (`bool`, *optional*, defaults to `False`):
                Whether to tokenize the output or not.
            **kwargs:
                Additional keyword arguments for the tokenizer's `apply_chat_template` method.
        """

        if chat_template is None:
            if self.chat_template is not None:
                chat_template = self.chat_template
            else:
                chat_template = self.default_chat_template

        return self.tokenizer.apply_chat_template(
            conversation, chat_template=chat_template, tokenize=tokenize, **kwargs
        )

    @property
    def default_chat_template(self):
        """
        This template formats inputs in the form of a chat history. For each message in the chat history:
        * the template will output the role of the speaker followed by the content of the message.
        * content can be a single string or a list of strings and images.
        * If the content element is an image, the template will output a sequence of <image> tokens and <fake_token_around_image> token before and after each image
        * The template will output an <end_of_utterance> token at the end of each message.

        Example:

        ```python
        messages = [{
            "role": "user",
            "content": [
                {"type": "text", "text": "What’s in this image?"},
                {"type": "image"},
                {"type": "image"},
                ],
        },
        {
            "role": "assistant",
            "content": [{"type": "text", "text": "This picture depicts Idefix, the dog of Obelix in Asterix and Obelix. Idefix is running on the ground."},]
        }]
        ```

        Will create outputs like:
        ```
        User: What is in this Image?<image><image><end_of_utterance>
        Assistant: This picture depicts Idefix, the dog of Obelix in Asterix and Obelix. Idefix is running on the ground.<end_of_utterance>
        ```
        """
        # fmt: off
        return (
            "{% for message in messages %}"
                "{{message['role'].capitalize()}}"
                "{% if message['content'][0]['type'] == 'image' %}"
                    "{{':'}}"
                "{% else %}"
                    "{{': '}}"
                "{% endif %}"
                "{% for line in message['content'] %}"
                    "{% if line['type'] == 'text' %}"
                        "{{line['text']}}"
                    "{% elif line['type'] == 'image' %}"
                        "{{ '<image>' }}"
                    "{% endif %}"
                "{% endfor %}"
                "<end_of_utterance>\n"
            "{% endfor %}"

            "{% if add_generation_prompt %}"
                "{{ 'Assistant:' }}"
            "{% endif %}"
        )
        # fmt: on